Advertisement

Efficient Landmark-Based Candidate Generation for kNN Queries on Road Networks

  • Tenindra Abeywickrama
  • Muhammad Aamir Cheema
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10178)

Abstract

The k nearest neighbor (kNN) query on road networks finds the k closest points of interest (POIs) by network distance from a query point. A past study showed that a kNN technique using a simple Euclidean distance heuristic to generate candidate POIs significantly outperforms more complex techniques. While Euclidean distance is an effective lower bound when network distances represent physical distance, its accuracy degrades greatly for metrics such as travel time. Landmarks have been used to compute tighter lower bounds in such cases, however past attempts to use them in kNN querying failed to retrieve candidates efficiently. We present two techniques to address this problem, one using ordered Object Lists for each landmark and another using a combination of landmarks and Network Voronoi Diagrams (NVDs) to only compute lower bounds to a small subset of objects that may be kNNs. Our extensive experimental study shows these techniques (particularly NVDs) significantly improve on the previous best techniques in terms of both heuristic and query time performance.

Keywords

Road networks Nearest neighbor Landmark Lower Bounds 

Notes

Acknowledgements

We sincerely thank the anonymous reviewers for their feedback which helped improve our work. The research of Muhammad Aamir Cheema is supported by ARC DE130101002 and DP130103405. Tenindra Abeywickrama is supported by an Australian Government RTP Scholarship.

References

  1. 1.
    Abeywickrama, T., Cheema, M.A., Taniar, D.: K-nearest neighbors on road networks: a journey in experimentation and in-memory implementation. PVLDB 9(6), 492–503 (2016)Google Scholar
  2. 2.
    Akiba, T., Iwata, Y.: Kawarabayashi, K.I., Kawata, Y.: Fast shortest-path distance queries on road networks by pruned highway labeling. In: ALENEX, pp. 147–154 (2014)Google Scholar
  3. 3.
    Erwig, M., Hagen, F.: The graph voronoi diagram with applications. Networks 36, 156–163 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Goldberg, A.V., Harrelson, C.: Computing the shortest path: a search meets graph theory. In: SODA, pp. 156–165 (2005)Google Scholar
  5. 5.
    Goldberg, A.V., Werneck, R.F.F.: Computing point-to-point shortest paths from external memory. In: ALENEX, pp. 26–40 (2005)Google Scholar
  6. 6.
    Kolahdouzan, M., Shahabi, C.: Voronoi-based k nearest neighbor search for spatial network databases. In: VLDB, pp. 840–851 (2004)Google Scholar
  7. 7.
    Kriegel, H.-P., Kröger, P., Kunath, P., Renz, M.: Generalizing the optimality of multi-step k-nearest neighbor query processing. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 75–92. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-73540-3_5 CrossRefGoogle Scholar
  8. 8.
    Kriegel, H.-P., Kröger, P., Renz, M., Schmidt, T.: Hierarchical graph embedding for efficient query processing in very large traffic networks. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 150–167. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-69497-7_12 CrossRefGoogle Scholar
  9. 9.
    Okabe, A., Boots, B., Sugihara, K.: Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, 2nd edn. Wiley, Hoboken (2000)CrossRefzbMATHGoogle Scholar
  10. 10.
    Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query processing in spatial network databases. In: VLDB, pp. 802–813 (2003)Google Scholar
  11. 11.
    Qiao, M., Qin, L., Cheng, H., Yu, J.X., Tian, W.: Top-k nearest keyword search on large graphs. PVLDB 6(10), 901–912 (2013)Google Scholar
  12. 12.
    Samet, H., Sankaranarayanan, J., Alborzi, H.: Scalable network distance browsing in spatial databases. In: SIGMOD, pp. 43–54 (2008)Google Scholar
  13. 13.
    Seidl, T., Kriegel, H.P.: Optimal multi-step k-nearest neighbor search. In: SIGMOD, pp. 154–165 (1998)Google Scholar
  14. 14.
    Shahabi, C., Kolahdouzan, M., Sharifzadeh, M.: A road network embedding technique for k-nearest neighbor search in moving object databases. GeoInformatica 7(3), 255–273 (2003)CrossRefGoogle Scholar
  15. 15.
    Zheng, B., Zheng, K., Xiao, X., Su, H., Yin, H., Zhou, X., Li, G.: Keyword-aware continuous kNN query on road networks. In: ICDE, pp. 871–882 (2016)Google Scholar
  16. 16.
    Zhong, R., Li, G., Tan, K., Zhou, L., Gong, Z.: G-tree: an efficient and scalable index for spatial search on road networks. TKDE 27(8), 2175–2189 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Monash UniversityMelbourneAustralia

Personalised recommendations